It sounds crazy, I know, but they figured out how to structure micro-tasks on Amazon Mechanical Turk such that they elicited empathetic responses and cognitive reappraisals from anonymous workers with no training in psychotherapy. For example, the system starts with a stressor text that a distressed user might enter, such as, “I’m going to flunk out of school and I’ll never get a job, I know it!”

Generating empathetic responses was fairly straightforward. They post the stressor comment and some guidelines for generating an empathetic response:

(1) address the user directly, (e.g., “Michael, I’m sorry to hear …”), (2) let the user know that his/her emotion makes sense, given the situation, and (3) share how you might feel if you were in a similar situation.

Turkers generate candidate responses and other Turkers vote on whether the candidate responses are appropriately empathetic. In an experiment, these responses were rated as much more empathetic than responses generated in response to the instruction to simply make the stressed user feel better about his/her situation (5.71 vs. 4.14 on a 7-point scale.)

Even more interestingly, the crowd could follow a structured process to generate cognitive reappraisals. They first ask some turkers to classify the stressor statement as having some cognition distortion or not. A distortion means, “logical fallacies within negative statements (Beck, 1979).” The example statement about flunking out never getting is a distortion because there’s no way the speaker could know that s/he’ll never get a job in the future. On average, workers made this binary classification correctly 89% of the time. Using several workers to classify a single statement could increase accuracy.

When the worker marks a statement as a cognitive distortion, they are asked to give a “thought-based reappraisal” explaining the nature of the distortion. No complex training is needed for the workers: they are simply given some sample responses for inspiration.

When the work does not indicate a distortion, the worker is asked to give a “situation-based reappraisal” that suggests a different way of thinking about the situation. Workers are introduced to the concept and given a few examples of good and bad appraisals (the latter are needed to dissuade workers from offering advice or making unrealistic assumptions about the original speaker’s situation, two common errors they observed.) Some workers were asked to come up with their own reappraisal suggestions, while others were asked to try specific strategies such as finding a silver lining or taking a long-term perspective.)

Responses were limited to four sentences. In the experiment, reappraisals solicited in the way described above were rated as better at offering a positive way to think about the situation (5.45 vs. 4.41) than when workers were asked to simply make the stressed user feel better about his/her situation.

Overall, this suggests that the crowd can, with little training, be a useful source of informational feedback and emotional support.

They gave pairs of people heart rate monitors and software that allowed to see various representations of the partner’s heart rate. Some pairs were intimate partners using the system at home, others were friends or colleagues using them at work.

One take-away is that people did not find the partner’s heart rate to be a useful informative signal. Without some context about the activity the partner was doing (walking up stairs? facing a difficult question at a presentation?) the heart rate fluctuations couldn’t be meaningfully interpreted. As one of their subjects put it, they could only be misinterpreted. But once the contextual information was known, the heartrate didn’t provide any additional useful information.

Despite the lack of useful information, the intimate couples reported that it was useful for creating a feeling of connection. The heart rate information might not have carried any cognitive signal, but just knowing that it reflected some physical part of the other person made them feel emotionally connected. I asked Petr if he thought it was especially important that it be the heart, and he wasn’t sure– he thought other signals like skin temperature or body velocity (moving or not) might create the same effect, though he thought the heart has special symbolic significance as carrier of emotions.

People also noted they did not like the loss of control over their self-presentation. They have learned to conceal some of their emotional reactions and didn’t like the prospect of the heart rate feedback giving them away. One exception was a couple that asked for a bunch of extra monitors to use in their weekly poker game with friends. They rigged it up so everyone could see everyone else’s heart rate while they played. That added an extra bit of challenge: you not only needed a poker face but also a poker heart rate, and you could try to read other people’s heart rates and make inferences about their cards.

Amusingly, a few of us at the conference had our own poker game last night. At some point, someone mentioned the far out idea of playing poker hooked up to various sensors. It’s a huge conference with 15 parallel activities at a time, and none of the other poker players had seen the presentation, so I had the pleasure of reporting that someone had already implemented the far out idea.

They had previously developed and reported on a system that lets people jog “together” though physically separated (even England to Australia!) They can talk with each other, but the sound is spatially located, so it sounds like your running partner is to your left (or right) and ahead of you or behind you. If they’re getting ahead of you, it can spur you to speed up, or slow down if they’re behind you.

Now they’ve gone for a “better than being there” experience. If your running pace is different than your partner’s, they can still have you run together. Instead of balancing your speed in order to stay next to your partner, you have to balance some other metric of exertion. In their version, each person picks their own maximum target heart rate, and you have to match the percentage of personal target in order to stay aurally next to your partner while jogging.

Pretty goal. Current prototypes use a little too much hardware for comfortable jogging, but I expect something like this will be available for iPhones some time.

In areas like smoking cessation and cancer screening, where the goal is to educate and get people to take the first steps toward behavior change, “tailored messaging” was developed in the 1990s to try to improve on the effectiveness of one-size-fits-all brochures that are often distributed in clinics. So far, however, the techniques of recommender systems (also called collaborative filtering) that I helped to develop, also starting in the early 1990s (recipient of ACM Software Systems Award last year) , don’t seem to have been applied in tailored health messaging. In this post, I’ll explore what has been tried in tailored health messaging, and where the opportunities might be to incorporate the recommender system techniques that are now ubiquitous in commerce and other applications on the Internet. Continue reading →

With online maps and GPS in our phones, it’s gotten a lot easier to record where we plan to go and where we’ve gone walking, running, or cycling. As a side effect of recording, we can also share our routes with others. Information on others’ routes can be surprisingly valuable, when you go to a new place, start a new activity, or just need some variety. I’ve found the MapMyWalk/Run/Ride family of sites to be very helpful for that. If you enter an address or city, it pulls up nearby routes that other people have mapped.